The R package BayesPPD (Bayesian Power Prior Design) supports Bayesian power and type I error calculation and model fitting after incorporating historical data with the power prior and the normalized power prior for generalized linear models (GLM). The package accommodates summary level data or subject level data with covariate information. It supports use of multiple historical datasets as well as design without historical data. Supported distributions for responses include normal, binary (Bernoulli/binomial), Poisson and exponential. The power parameter can be fixed or modeled as random using a normalized power prior for each of these distributions. In addition, the package supports the use of arbitrary sampling priors for computing Bayesian power and type I error rates. In addition to describing the statistical methodology and functions implemented in the package to enable sample size determination (SSD), we also demonstrate the use of BayesPPD in two comprehensive case studies.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2023-016.zip
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For attribution, please cite this work as
Shen, et al., "BayesPPD: An R Package for Bayesian Sample Size Determination Using the Power and Normalized Power Prior for Generalized Linear Models", The R Journal, 2023
BibTeX citation
@article{RJ-2023-016,
author = {Shen, Yueqi and Psioda, Matthew A. and Ibrahim, Joseph G.},
title = {BayesPPD: An R Package for Bayesian Sample Size Determination Using the Power and Normalized Power Prior for Generalized Linear Models},
journal = {The R Journal},
year = {2023},
note = {https://doi.org/10.32614/RJ-2023-016},
doi = {10.32614/RJ-2023-016},
volume = {14},
issue = {4},
issn = {2073-4859},
pages = {335-351}
}